Beyond visual fidelity, we study the models under the potentially competing aims of content variability on the one hand and semantic consistency on the other. To assess consistency, we compare the input layouts \(S\)  with those implied by the synthesized images, using standard metrics of mean IoU, pixel accuracy and ARI. Following prior works [1]}, [2]}, the inferred layouts \(S^{\prime }\)  are obtained by a pre-trained segmentation model that we use for evaluation purposes. To measure diversity, we sample \(n=20\)  images \({X_i}\)  for each input layout \(S\) , and compute the mean LPIPS pairwise distance [3]} over image pairs \(X_i,X_j\) . Ideally, we expect a good model to achieve strong alignment between each input layout and the one derived from the output synthesized image, while still maintaining high variance between samples that are conditioned on the same source layout.
